Lesson 2: Adding Mining Models to the Bike Buyer Mining Structure

In this lesson, you will add two mining models to the Bike Buyer mining structure that you created Lesson 1: Creating the Bike Buyer Mining Structure. These mining models will allow you to explore the data using one model, and to create predictions using another.

To explore how potential customers can be categorized by their characteristics, you will create a mining model based on the Microsoft Clustering Algorithm. In a later lesson, you will explore how this algorithm finds clusters of customers who share similar characteristics. For example, you might find that certain customers tend to live close to each other, commute by bicycle, and have similar education backgrounds. You can use these clusters to better understand how different customers are related, and to use the information to create a marketing strategy that targets specific customers.

To predict whether a potential customer is likely to buy a bicycle, you will create a mining model based on the Microsoft Decision Trees Algorithm. This algorithm looks through the information that is associated with each potential customer, and finds characteristics that are useful in predicting if they will buy a bicycle. It then compares the values of the characteristics of previous bike buyers against new potential customers to determine whether the new potential customers are likely to buy a bicycle.

You can now add a mining model to the Bike Buyer mining structure based on the Microsoft Clustering algorithm. Because the clustering mining model will use all the columns defined in the mining structure, you can use a shortcut to add the model to the structure by omitting the definition of the mining columns.

To add a Clustering mining model

In Object Explorer, right-click the instance of Analysis Services, point to New Query, and then click DMX to open Query Editor opens and a new, blank query.

Copy the generic example of the ALTER MINING STRUCTURE statement into the blank query.